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OpenCV with Python By Example

OpenCV with Python By Example

By : Prateek Joshi
3.5 (10)
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OpenCV with Python By Example

OpenCV with Python By Example

3.5 (10)
By: Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.
Table of Contents (14 chapters)
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13
Index

Blurring

Blurring refers to averaging the pixel values within a neighborhood. This is also called a low pass filter. A low pass filter is a filter that allows low frequencies and blocks higher frequencies. Now, the next question that comes to our mind is—What does "frequency" mean in an image? Well, in this context, frequency refers to the rate of change of pixel values. So we can say that the sharp edges would be high frequency content because the pixel values change rapidly in that region. Going by that logic, plain areas would be low frequency content. Going by this definition, a low pass filter would try to smoothen the edges.

A simple way to build a low pass filter is by uniformly averaging the values in the neighborhood of a pixel. We can choose the size of the kernel depending on how much we want to smoothen the image, and it will correspondingly have different effects. If you choose a bigger size, then you will be averaging over a larger area. This tends to increase...

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OpenCV with Python By Example
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